The #168 and #177 refactorings caused this regression. A new end-to-end
test has been added for proper coverage.
(This fix will be revisited at some point)
The former one was generated using SDXL 0.9 vs 1.0. The new one has been
generated with diffusers:
import torch
from diffusers import StableDiffusionXLPipeline, DDIMScheduler
noise_scheduler = DDIMScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
steps_offset=1,
)
base_model_path = "/path/to/stabilityai/stable-diffusion-xl-base-1.0"
device = "cuda"
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
seed = 2
pipe = StableDiffusionXLPipeline.from_pretrained(base_model_path, scheduler=noise_scheduler, torch_dtype=torch.float16, add_watermarker=False)
pipe = pipe.to(device)
generator = torch.Generator(device).manual_seed(seed)
images = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=30, generator=generator).images
This generalizes the Adapter abstraction to higher-level
constructs such as high-level LoRA (targeting e.g. the
SD UNet), ControlNet and Reference-Only Control.
Some adapters now work by adapting child models with
"sub-adapters" that they inject / eject when needed.
